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Sumblr: Continuous Summarization of Evolving Tweet Streams

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1 Sumblr: Continuous Summarization of Evolving Tweet Streams
Date: 2014/08/11 Author : Lidan Shou, Zhenhua Wang, Ke Chen, Gang Chen Source: SIGIR’13 Advisor: Jia-ling Koh Speaker: Sz-Han,Wang

2 Outline Introduction Method Experiment Conclusion
Tweet Stream Clustering High-level Summarization Experiment Conclusion

3 Introduction With the explosive growth of microblogging services, short text messages (also known as tweets) are being created and shared at an unprecedented rate. Tweets in its raw form can be incredibly informative, but also overwhelming. Plowing through so many tweets for interesting contents would be a nightmare, not to mention the enormous noises and redundancies that one could encounter.

4 Introduction In this paper, we study continuous tweet summarization as a solution. Traditional document summarization methods focus on static and small-scale data. Propose a novel prototype called Sumblr ( SUMmarization By stream cLusteRing) for tweet streams. A timeline example for topic “Apple”

5 Framework

6 Outline Introduction Method Experiment Conclusion
Tweet Stream Clustering High-level Summarization Experiment Conclusion

7 Tweet Cluster Vector Alice: a b c b e a e b.
a tweet ti =(tvi, tsi,wi) Alice: a b c b e a e b. tvi=[ ] For a cluster C containing tweets t1, t2,… tn Tweet Cluster Vector(TCV)(c)=(sum_v,wsum_v,ts1,ts2,ft_set) sum_v= i=1 n tvi ||tvi|| , wsum_v= i=1 n wi∙tvi The vector of cluster centroid(cv)= i=1 n wi∙tvi n = wsum_v n a b c e 1.301 1.477 1 TF-IDF score tvi:the textual vector,tsi:the posted timestamp wi : the UserRank value of the tweet’s author ts1= i=1 n tsi is the sum of timestamps ts2= i=1 n (tsi)2 is the quadratic sum of timestamps ft_set is a focus tweet set of size m, consisting of the closest m tweets to the cluster centroid (use cosine similarity as the distance metric)

8 Tweet Cluster Vector t1-Alice: a b c b e a e b. t2-Tim : a c c d d b e. t3-Judy: b c d e a a a. t4-Tina : b b d e e b b. t5-Sam : c c c b b b . a b c d e |tvi| t1 1.301 1.477 1 2.563 t2 2.527 t3 2.486 t4 1.602 2.293 t5 2.089 sum_v= i=1 n tvi ||tvi|| sim(cv,ti) t1 0.934 t2 0.951 t3 0.943 t4 0.815 t5 0.757 a b c d e sum_v 1.497 2.780 2.014 1.353 1.873 Suppose m=3: ft_set = {t2, t1, t3} wsum_v= i=1 n wi∙tvi a b c d e wsum_v 3.778 6.556 4.778 3.301 4.602 sim(cv,ti) cv= wsum_v n a b c d e cv 0.756 1.311 0.956 0.660 0.920

9 Pryamidal Time Frame The Pyramidal Time Frame (PTF) stores snapshots at differing levels of granularity depending on the recency. The maximum order of any snapshot stored at T is log𝛼(T); The maximum number of snapshots maintained at T is (𝛼𝑙+1) ‧ log𝛼(T) Each snapshot of the i-th order is taken at a moment in time when the timestamp from the beginning of the stream is exactly divisible by αi Each i-th order stored the maximum number of snapshots is (𝛼𝑙+1) 𝛼=3,𝑙=2 Start timestamp=1 Current timestamp=86 log3 (86) ≈ 4.05 (32+1)*log3 (86) ) ≈ 40.5 (32+1)=10

10 Tweet Stream Clustering
Intialization Use a k-means clustering algorithm to create the initial clusters Incremental Clustering MBS(Minimum Bounding Similarity)=β∙ Sim c1, ti Sim c1, ti = 1 𝑛 i=1 n tvi∙𝐶1 ||tvi||∙||𝐶1|| = wsum_v∙sum_v n∙||wsum_v|| c1 t1, t2, t3, t4, t5 TVC(1) Max Sim(c1,t) MaxSim(c1, t) < MBS → t is upgraded to a new cluster MaxSim(c1, t) ≥ MBS → t is added to its closest cluster c2 t6, t7, t8 TVC(2) Sim(c2,t) t Sim(c3,t) c3 t9, t10 TVC(3)

11 Tweet Stream Clustering
Restrict the number of active clusters Deleting Outdated Clusters - periodical examination Avgp > threshold → remove the cluster Merging Clusters - memory limit is reached Merging process continues until there are only mc percentage of the original clusters left threshold=3 days, p=10 Suppose mc=0.7, Remove:10*(1-0.7)=3 cluster Before Merging:c1,c2,c3,c4,c5,c6,c7,c8,c9,c10 cluster pairs distance (c1,c2) (c2,c4) (c1,c4) (c5,c7) (c4,c5) …… {c1,c2} {c1,c2,c4} {c5,c7} After Merging:{c1,c2,c4},c3,{c5,c7},c6,c8,c9,c10

12 High-level Summarization
Online summaries Retrieved directly from the current clusters maintained in the memory Historical summaries Retrieved two snapshots from PTF TCV-Rank Summarization

13 TCV-Rank Summarization
Generate input cluster Gather tweets from the ft_sets in D(c) as a set T S(ts1) TCV(C2) ft_set:{t4,t5} TCV(C3) ft_set:{t6,t7} the ending timestamp of the duration TCV(C1) ft_set:{t1,t2,t3} S(ts2) TCV(C5) ft_set:{t9,t10} TCV(C4) ft_set:{t1,t2,t8} TCV(C6) ft_set:{t11} the beginning timestamp of the duration TCV(C1-C4) ft_set:{t3} TCV(C1-C4) ft_set:{t3} input cluster D(c) TCV(C2) ft_set:{t4,t5} TCV(C3) ft_set:{t6,t7} TCV(C4) ft_set:{t1,t2,t8} TCV(C5) ft_set:{t9,t10} TCV(C6) ft_set:{t11} T={t1,t2,t3,t4,t5,t6, t7,t8,t9,t10,t11}

14 TCV-Rank Summarization
Build a cosine similarity graph on T Compute LexRank scores LR Add tweet t into the summary 𝑡= argmax 𝑡𝑖 [𝜆 𝑛𝑡𝑖 𝑛𝑚𝑎𝑥 𝐿𝑅 𝑡𝑖 − 1−𝜆 avg 𝑡𝑗∈𝑆 𝑆𝑖𝑚 𝑡𝑖,𝑡𝑗 ] T={t1,t2,t3,t4,t5,t6,t7,t8,t9,t10,t11} tvi t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 LR 0.601 0.847 0.349 0.752 0.591 0.799 0.355 1 0.592 0.691

15 LexRank Build cosine similarity Matrix and degree
LR=PowerMethod(M,n,𝜖) Matrix M t1 t2 t3 t4 1 0.8 0.6 0.3 0.7 0.4 0.9 i degree t1 3 t2 t3 4 t4 2 t1 t2 t3 t4 0.33 0.27 0.15 0.18 0.2 0.23 0.25 0.45 0.1 0.13 0.5 Sim[i][j] > t (t=0.5) 𝑠𝑖𝑚 𝑖 [𝑗] 𝑑𝑒𝑔𝑟𝑒𝑒[𝑖] pt 0.25 pt+1 0.23 0.24 0.20 0.33 𝛿=||pt+1-pt|| Compare 𝛿 and 𝜖 if 𝛿<𝜖, pt+1=LR pt+1=MTpt

16 Topic Evolvement Detection
Continuous timeline Compute Dcur and Davg if Dcur Davg > 𝜏 , add time node Sp Kullback–Leibler divergenc DKL(Sc||Sp) = w∈V p(w|Sc) ln p(w|sc) p(w|sp) Current summary Add to timeline Sc current summary The iPhone 6 release date will be in 2014

17 Outline Introduction Method Experiment Conclusion
Tweet Stream Clustering High-level Summarization Experiment Conclusion

18 Experiment Datasets Baseline ClusterSum LexRank DSDR

19 Experiment windows size=20000 step size=4000~20000

20 Outline Introduction Method Experiment Conclusion
Tweet Stream Clustering High-level Summarization Experiment Conclusion

21 Conclusion Proposed a prototype called Sumblr which supported continuous tweet stream summarization. Sumblr employed a tweet stream clustering algorithm to compress tweets into TCVs and maintain them in an online fashion. Used a TCV-Rank summarization algorithm for generating online summaries and historical summaries with arbitrary time durations. The topic evolvement could be detected automatically, allowing Sumblr to produce dynamic timelines for tweet streams. For future work, we aim to develop a multi-topic version of Sumblr in a distributed system, and evaluate it on more complete and large-scale datasets.

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